Distributed, Collaborative Data Analysis from Heterogeneous Sites Using a Scalable Evolutionary Technique

摘要

This paper documents an early effort to develop an experimental, collaborative data analysis technique for learning classifiers from a collection of heterogeneous datasets distributed over a network. The proposed technique makes use of a scalable evolutionary algorithm, called the GEMGA to classify datasets. This paper describes the developed technique and the results of the use of this technique through the application of this system for several domains, including distributed fault detection in an electrical power distribution network.